Many clinical studies require following subjects over time and measuring the time until a certain event is experienced (e.g., death, progression, hospital discharge, etc). The resulting collected datasets are typically analyzed with survival analysis techniques.
Survival analysis is a branch of statistics that analyzes the expected duration until an event of interest occurs [1]}. Censoring is an essential concept in survival analysis which makes it challenging compared to other analytical methods. Censoring can occur due to various reasons, such as drop-out, and means that the observed time is different from the actual event time. In the case of right censoring, for instance, we know that the actual event time is greater than the observed time [2]}.
